An integrative information aqueduct to close the gaps between global satellite observation of water cycle and local sustainable management of water resources
Project Summary
The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources?Â
iAqueduct aims to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources, and will address the abovementioned scientific questions by combining medium-resolution (10 mâ1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms.
iAqueduct complements the actions developed under the European Strategy Forum for Research Infrastructures (ESFRI) by coordinating a set of European research groups and sites allowing the scaling up to pan-European level under the aegis of the COST action Harmonization of UAS techniques for agricultural and natural ecosystems monitoring (HARMONIOUS) in which 70 institutions from 32 countries participate.
iAqueduct collaboration, coordination, mobility, synergies
HARMONIOUS - IAQUEDUCT Joint Fieldwork
iAqueduct Consortium
iAqueduct Observatories & Data Repository
This study area partly belongs to the âCilento and Vallo di Dianoâ National Park, the largest national park in Italy, and is included as a representative site within the UNESCO-HELP program. The Alento River Catchment is usually split in the Upper Alento, a hilly and mountain marginal area that suffered from severe land abandonment and subsequent land-use changes, and the Lower Alento, characterized by a flourishing economy especially because of tourism along the entire coastline. A system of barrages, the largest being the âPiano della Roccaâ earthen dam, were built and is managed by the âVeliaâ Bureau of Reclamation to increase irrigated agriculture and the quality of livestock methods, hence reducing the gap between the two parts of the catchment.
However, as in most water-stressed zones of the Mediterranean belt, this area is experiencing an excessive demand for water partly because of the competition among different users, which could yield conflicts among them, especially during summer. Decision-makers and stakeholders are now concerned about future benefits and constraints deriving from the changes observed in land uses and climate seasonality, and are therefore interested in addressing the following main issues: a) Predicting the storage capacity of the artificial reservoirs in view of projected climate and land-use changes so as to meet short- and medium-term water requirements from households, agriculture, tourism, and hydropower generation; b) promoting the most effective demand-side adaptation options; and c) identifying optimal land resource management to ensure adequate water availability to all sectors, reduce fire risk during the prolonged dry seasons, and, at the same time, alleviate natural hazards, such as flooding and soil erosion, during the wet season.
To meet these needs, the Alento River catchment is becoming a science-driven critical zone observatory (CZO), with a major aim of supporting the issues of rural environmental protection and sustainable management of natural resources. The âAlentoâ CZO not only relies on background geological, pedological, and hydrological studies carried out over the last decades but also benefits from a series of investigations currently underway in the Upper Alento catchment. Since 2016, wireless sensor networks (WSNs) and cosmic-ray neutron probes (CRNPs) monitor soil moisture in two small sub-catchments, named MFC2 and GOR1, having different topographic, pedological, and land-use characteristics as well as slightly different weather conditions.
On the left: Alento upper catchment (with the black boundary), as well as the locations of MFC1, MFC2, and GOR1 sub-catchments. On the right: the MFC2 area, and the detailed locations of SoilNet units, the cosmic ray probe and the surface hydrological boundaries.
Barranco del Carraixet (or Carraixet Creek) is located in the east coast of Spain, has a catchment area of 314 km2, draining directly to the Mediterranean Sea, with a natural park in the upper part of the basin and with anthropogenic pressures in the middle and low basin. The human effect is quite important in this study site: The lowlands are characterised by alternation of the urban and industrial zones and agricultural fields, while the upper part is frequently affected by wildfires and it is a highly frequented leisure zone, subject to multiple pressures (hunters, several outdoor sports, owners, etc.). The climate is semiarid Mediterranean, with a mean annual precipitation of around 400 mm highly variable and potential evapotranspiration of 1100 mm. The hydrology is characterised by low or absent base flow, typical of Mediterranean ephemeral streams. Urban and irrigation water demands are supplied by the aquifer, mainly recharged by the upper catchment. The actual trend of the catchment is towards forest expansion in abandoned lands of the upper part and urbanization in the lower part. The main concern in Carraixet Creek is to improve forest management in order to increase aquifer recharge, increase the forest health, and to better control soil erosion. For this project, we will consider the upper and medium parts of the catchment, with an area of 250 km2. Within this area, there is one experimental watershed of 1 km2Â (with 3 meteorological stations, 1 cosmic ray, and 1 flowgauge) and 1 experimental forest plot heavily sensorized. At the catchment scale and operated by the Jucar Basin Water Authority, there is one additional flowgauge station and several raingauges and piezometric observations.
Fiumarella of Corleto belongs to the Basilicata region that is characterized by a significant diversity in terms of climatic conditions. For instance, the mean annual rainfall ranges between 400 and 2000mm. Such variability reflects the regional hydrological patterns with areas affected by droughts and others that experience several floods and landslides. In this context, the study of river basin hydrology becomes critical from several points of view. The Fiumarella of Corleto is located in the water-rich part of the region that is crucial for the water supply of the region but also for the water supply of the Puglia region, which strongly relies on external resources for their agricultural and economical activities.
The experimental basin âFiumarella of Corletoâ, located in Basilicata region (southern Italy), is a tributary of the Sauro river (Agri basin) and has an area of 32.5 km2. It is situated in a sub-humid climatic zone with a mean annual rainfall of approximately 720 mm and characterized by hot-humid summers and chilly to mild winters. The interest towards this basin is due to its peculiarities. In fact, the two slopes of the catchment have different land uses: The slope on the left is covered mostly by forests, the slope on the right is covered by agricultural land. In order to characterize with a high level of details the morphology of the two slopes, a DSM of the basin at high-resolution (1 Ă 1 m) was derived with a LiDAR.
Kibbutz Sde Yoav is an agricultural settlement located in south-central Israel, between the cities of Ashkelon, Kyriat Gat, and Kyriat Malakhi. Like Sde Yoav, in Israel, there are several agricultural settlements that were created during the establishment of the state of Israel in order to ensure the food supply. In order to monitor the fields that sustain these settlements, farmers need chemical/physical analyses. However, traditional soil survey methods are expensive, time-consuming, and need high skilled professionals. Moreover, in order to represent these parameters spatially in these large agricultural fields, it is necessary to take several samples for a correct kriging methodology, and the measurements in question varies seasonally. Additionally, farmers cannot see the status of every point of interest in these fields with a common frequency. Given the lack of rains and the dry climate of the region, water is a critical resource that is necessary to manage carefully. Remote sensing is a potential solution for this problem, because it can replace field chemical/physical measurements, and could monitor the infiltration rate in the fields of interest in a spatial scale.
Water safety and climate change have emerged as one of the first public concerns in the last years in the Twente area, the Netherlands. The major challenge is water management under climate change that needs to take into account periods of extremes, such as when it rains more and harder and when longer periods of drought persist, in maintaining the safeties and functionalities of the quays, dikes, weirs, and pumping stations. To meet this challenge, local and regional monitoring of the actual state of the water system and the anticipation of near future situations are needed. Agricultural water management requires, on the other hand, operational management of soil and water for adequate agricultural productions in the growth seasons at a field level. Other requirements are related to water quality management for nature conservation, including water treatments. A shortage of precipitation (i.e., precipitation minus potential evaporation) is used as a measure for water excess or shortage for the abovementioned tasks.
A regional soil moisture monitoring network has been installed since 2009 in the Twente region of The Netherlands, consisting of 20 stations continuously measuring soil moisture and soil temperature over an area of approximately 50 km Ă 40 km. The main objectives of Twente monitoring network are: i) To investigate the sensitivity of active and passive microwave data to surface parameters, such as soil moisture, soil temperature, and vegetation cover; ii) to run, calibrate, and validate new soil moisture retrieval algorithms; and iii) to study new approaches to upscale soil moisture information from the point to large scale.
One of the main threats in the catchment of Zala river is the more frequently occurring extreme weather events at the catchment. Information on the impact of potential future climate changes to water resources and possible management scenarios to adapt to future extreme weather events will be shared with the General Directorate of Water Management, the farmer organizations (e.g., AGRYA, Agrion Top Kft.) and public bodies (e.g., Hungarian Chamber of Agriculture, Zala County, Zala County Office of Agricultural and Rural Development Agency). At the catchment, it is also important to analyse how the transport of fertilizers, pesticides, and herbicides will change due to the extreme weather events. Recently, the amount of nitrate and pesticide in the groundwater at the catchment is close to the threshold value of groundwater pollution, and at a few plots, even exceeds it.
The catchment of river Zala in western Hungary belongs to the watershed of Lake Balaton. The catchment area of the Zala River is 2622 km2, it is situated in Zala Hills. Mean discharge of Zala is 5.6 m3Â sâ1. The climate is moderately warm, moderately humid, and the number of sunshine hours per year ranges between 1800 and 2000 h. The mean annual temperature of the region is about 10 ËC. The average amount of rainfall is between 600 and 700 mm yearâ1.
The figure describes the iAqueduct framework of methodologies and approaches. It includes six closely connected working packages (WPs). WP1 deals with the scaling from global satellite water cycle products to field-scale water states, which includes both the surface and profile information on soil water states. WP2 will apply pedotransfer functions to derive local field-specific SHP/STP properties for the modelling of soil water and heat dynamics at field-scale precision. WP3 attempts to retrieve field- and grid-specific relationship functions between soil properties, soil moisture, and evapotranspiration. WP4 is expected to advance ecohydrological modelling by intercomparing models with different levels of complexity, in terms of the soilâwaterâvegetationâatmosphere transfer processes involved. WP5 will then demonstrate the benefits in closing water cycle gaps from the global to local scale, in terms of how to effectively handle spatiotemporal data (from in situ, UAS, and satellites), regarding ecohydrological model calibrations and accuracy evaluations of simulated spatial patterns of ecohydrological variables. WP6 is about disseminating and communicating generated knowledge, data, and tools to water managers, companies, and farmers for actual sustainable water management of their responsible domains.Â
Lead: Salvatore Manfreda (Uni. Naples)
Participate: Uni. Twente, UPV, Uni. Basilicata
This WP will focus on the monitoring and downscaling of soil moisture data based on remotely sensed data. It is divided into two main trajectories: one aimed at the spatial description of soil moisture and the second focusing on the prediction of soil moisture in the root-zone. The WP will be developed in close connection with the activities of WP2.Â
Task 1.1 Spatial downscaling procedures and data products
(a) Soil moisture downscaling workflow based on random forest regression (RF); (b) The importance of land surface features for the RF model; (c) RF-based downscaling of Sentinel-1 soil moisture products at 1km to 15cm, taking land surface features derived from UAS as predictors over the MFC2-Alento catchment. The UAS thermal image taken at sunrise 05:13 14 June 2019 was used to derive LST, the multispectral image taken 15:42, 13 June 2019 was used to derive NDVI; (d) the comparison of the downscaled soil moisture with in situ measurements. (see Publication 1)
Task 1.2 Derive profile soil water content from surface soil moisture information
The example workflow for deriving root zone soil moisture (RZSM) from surface soil moisture (SSM), which results in ~10-year consistent surface and root zone soil moisture over Tibetan Plateau (see publication 2).Â
Lead: Brigitta SzabĂł (CAR-HAS)
Participate: TAU, Uni. Naples, Uni. Twente
WP2 will retrieve soil hydraulic and thermal parameters (SHP/STP) from spectral signatures and knowledge of near-surface soil moisture dynamics. This WP will initially employ commonly used pedotransfer functions (PTFs) for regional applications and then locally calibrate them using readily measurable surface soil spectral features. The world Soil Spectral Library (SSL, Rossel et al. 2016, ERS), European Spectral Soil Library (LUCAS, Toth et al., 2013, EMA) and some local SSL (e.g. the GEO-CRADLE Mediterranean Balkan SSL) will be used to generate global to local spectral based models to assess soil properties. A harmonised protocol will be developed from the selected sites.
Task 2.1 Collection of field scale data
Figure: Map of the MFC2-Alento catchment. Red crosses indicate the locations of SoilNet sensors installed at soil depths of 15 cm and 30 cm. The positions of the SoilNet sensors correspond to soil sampling locations. The RGB-VIS coverage area is 18 ha, and the thermal and hyperspectral coverage area is 7.5 ha.
Upper figure: Overview of equipment for UAS flights including DJI Phantom 3 Pro (a) and payload: Tetracam ADC Snap camera (b) and FIR Tau2 camera (c).
Bottom Figure: Hyperspectral data collection with Cubert UHD-185 hyperspectral camera on UAS platform
Figure: The impression of the number of UAS photos taken during a flight.
Figure: Different land surface features are retrieved from different UAS imageries
Task 2.2 Soil spectroscopy and hyperspectral remote sensingÂ
Figure:Â IDW interpolations of the measured against the predicted WIR (water infiltration rate) (based on Cubert UHD-185 hyperspectral Imageries) in the study site of Alento.
Task 2.3 Basic pedotransfer functions
Figure: Comparison between observed and predicted soil WRF for sample#5 (texture classes, soil bulk density, and organic matter content are reported on the plot). WRF data pairs are built by obtaining soil water content values at 30 prescribed pressure head values (in most PTFs).
Task 2.4 Advanced pedotransfer functions
Hyperspectral data will be used to derive spectrotrasfer functions (STF) and spectral pedotransfer functions (SPTFs) using soil and environmental data as well. Such function will be used for the description and mapping of topsoil hydraulic properties with high level of details. Further to the site specific STFs the relationship between SSLs and hydraulic properties will be analysed on European datasets (LUCAS, EU-HYDI). Hyper- and multi-spectral sensors on ground and UAS will be employed for in-situ prediction of soil organic carbon (SOC), soil sealing and soil particle size distribution by visâNIR spectroscopy. Soil hydraulic properties will be mapped for the study sites with SPTFs, STFs based on the available data, similarly to Task 2.3. Validation will be performed using measured values obtained in Task 2.1. Such task will support the downscaling procedures described in WP1 - Task 1.1.
Lead: Nunzio Romano (Uni. Naples)
Participate: Uni. Twente, UPV, SLU
This WP concerns the retrieval of field/grid specific scaling functions (task 3.1) and their generalization (task 3.2).Â
Task 3.1 Field/grid specific scaling functions between soil moisture and evapotranspiration:Â
Figure. (a) KoeppenâGeiger climate classifications across the WaPOR domain of the Africa and the Middle-East; (b) Mean Actual Evapotranspiration (AET) versus root zone relative soil moisture (i.e., saturation degree) stratified by the KoeppenâGeiger climate classifications (Su et al. 2020, Water).
 
Figure: sand fraction (left) and clay fraction (right) of Alento MFC2 catchment
Figure. STEMMUS model to be applied over Alento MFC2 catchment (https://blog.utwente.nl/stemmus/)
Task 3.2 Generalizing scaling functions between soil moisture and evapotranspiration
Figure. The coupled STEMMUS-SCOPE model will be applied over iAqueduct study sites, the output of which will be used for generalizing the scaling function (Wang, et al. 2021, GMD).
Lead: Giulia Vico (SLU)
Participate: Uni. Naples, Uni. Twente, UPV
WP4 intercompares different models, soil and vegetation parametrizations and parameters. Analysis of water flow processes will be made, with the different models used in the different sites, e.g. in the soil-vegetation-atmosphere (SVA) system making use of the detailed field observations available in ARHO and integration of the data into a process-based ecohydrological model, considering also validation and output uncertainties. To facilitate the integration of models into the iAqueduct toolbox (Task 4.2), particular attention will be devoted to identify models that provide robust realistic results, while at the same time having low parameter requirements and easy transferability across sites. To this aim, a minimalist soil-vegetation-atmosphere model will be developed and its applicability across sites assessed, employing the data collated within this project.Â
Figure: WUE- Water use efficiency model; CM â Carbon Maximization; SOX â Stomatal Optimization based on xylem hydraulics (Bassiouni et al. 2021, New Phy.)
Figure: STEMMUS is coupled with T&C model for understanding the impact of detailed soil processes on ecosystem water, energy, and carbon fluxes (Yu et al. 2020, TC).
Lead: Félix Francés (UPV)
Participate: Uni. Twente, Uni. Naples, Uni. Basilicata, Uni. Twente, CAR-HAS, TAU
The aim of this WP is closing water cycle gaps by improving hydrological model implementations using spatial information. This WP will advance how to effectively handle spatio-temporal data when included in model calibration and how to evaluate the accuracy of the simulated spatial patterns.Â
Figure: TETIS model and parameters, as well as the spatially distributed data used to run the model.
Figure: TETIS model calibration using remote sensing data
Lead: Bob Su (Uni. Twente)
Participate: Uni. Naples, Uni. Basilicata, CAR-HAS, TAU, UPVTask 6.1 iAqueduct toolboxÂ
Task 6.1 iAqueduct toolboxÂ
The results of analysis in previous WPs will be integrated into a library as the iAqueduct toolbox which consists of water flow processes in relations to the models, soil and vegetation parametrizations and soil parameters as well as forcing fields. The existing openâsource software system MajiSys water information system at University of Twente will serve as the integration platform. Such a toolbox will then be used for robust application (incl. machine learning algorithms) to other sites and also for use by stakeholders (See WP5 and WP6).
- Soil moisture downscaling
The following link is a demo on downscaling raster images, which is a preliminary result of the iAqueduct modelling and processing development. The demo will open in a new tab. http://iaqueduct.itc.utwente.nl/iaqueduct/
- TETIS model
The following link is a demo on the TETIS model. It will eventually become part of the iAqueduct Toolbox. The demo will open in a new tab. http://iaqueduct.itc.utwente.nl/tetis/
Task 6.2 Case studies
Figure Drought analysis and risk assessment